arXiv:2605.31464v1 Announce Type: new Abstract: GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measurement on target hardware. While these measurements provide the ground-truth signal necessary for kernel search, they are costly, because each evaluation of a kernel requires compilation and repeated execution on a GPU. As improvements in LLM inference reduce the cost of writing novel kernels and LLM-driven searches scale to large search budgets, on-device evaluation becomes a bottleneck. To address
The increasing complexity of GPU kernels and the rising cost of on-hardware evaluation for LLM-driven optimization necessitate more efficient methods to identify performance bottlenecks without extensive physical testing.
Strategic readers should care because this innovation can significantly accelerate the development and optimization of AI models, directly impacting the efficiency and cost of AI compute infrastructure and the pace of AI innovation.
The reliance on repeated physical measurements for GPU kernel optimization is being reduced by selective surrogates (language models), making the optimization process faster and less resource-intensive.
- · AI developers
- · Cloud computing providers
- · GPU manufacturers (indirectly)
- · Deep learning researchers
- · Traditional hardware measurement R&D
- · Companies with inefficient optimization pipelines
Faster and cheaper development cycles for cutting-edge AI models.
Increased competition in AI model deployment as optimization becomes less of a barrier.
Potential for an acceleration in the discovery of novel hardware architecture optimizations, guided by LLMs, that are difficult to find manually.
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Read at arXiv cs.LG